How an AI agent can convert PostHog experiment context into a readiness brief with metrics, risks, instrumentation gaps, and next actions
This workflow gives an AI agent a defined job, a bounded set of records, and a result a person can review. The agent reads the relevant PostHog context, applies the rules in the prompt, and keeps the source behind every recommendation. It returns a proposed handoff rather than taking consequential actions on its own.
Can an AI agent convert PostHog experiment context into a readiness brief with metrics, risks, instrumentation gaps, and next actions?
Yes. Start with the scope, date range, decision rules, and fields that identify the right records. The agent can collect the evidence, compare states or sources, mark conflicts and missing data, and organize the result around the outcome above. A reviewer then checks the matches and judgment calls before approving messages, record updates, bookings, purchases, publishing, or other write actions. The guide below shows the records, boundaries, prompt, and handoff needed for this specific workflow.
What this agent helps you do
A PostHog experiment readiness agent reviews whether an experiment is ready to launch or interpret. It checks target metrics, exposure, feature flags, instrumentation, cohorts, and decision risks.
When to use this workflow
Use it before launching an A/B test, expanding a rollout, reviewing experiment health, or presenting early results.
How PostHog gives the agent context
Connect PostHog and define the project, experiment, feature flag, metric, cohort, and timeframe. Ask the agent to distinguish measured evidence from hypotheses and to call out missing instrumentation.
Example starter prompt
Review this PostHog experiment for readiness. Summarize setup, primary metrics, exposure risks, instrumentation gaps, feature flag concerns, and recommended next actions.Suggested workflow steps
The agent gathers experiment context, checks metric and rollout assumptions, identifies data gaps, and prepares launch or analysis recommendations.
Verify the PostHog feature flag, exposure event, unit of analysis, primary metric definition, property filters, attribution window, and exclusion rules. A metric appearing on a dashboard does not prove it measures the experiment population correctly.
Run sample queries for exposure and outcome events before launch, then inspect duplicate, missing, and out-of-order events. Keep the decision to start, stop, or ship the experiment with the product owner.
Questions this workflow answers
Are we ready to launch this product experiment and trust the result it will produce?
The agent reviews the experiment from assignment through decision. It records the hypothesis, population, unit of assignment, variants, traffic allocation, exposure definition, primary metric, guardrails, duration assumptions, and decision owner. If one of those pieces is missing, the readiness report describes the consequence. A test without a stable exposure event can compare people who never saw the change; a metric without a fixed attribution window can drift during analysis.
Before launch, the agent runs or proposes small checks with test users. It verifies that assignment and exposure occur once under the expected conditions, variant properties are present, outcome events arrive with the required values, and bot, employee, or other excluded traffic follows the written rule. It inspects duplicate and out-of-order events and checks whether identity changes could put one person in both variants. Dashboard visibility alone does not answer these questions.
Metric review focuses on definitions. “Activation” needs an event or formula, filters, aggregation, unit, and window. The agent shows whether the existing insight uses that same definition and whether historical volume is sufficient for a meaningful test plan without inventing statistical certainty. Secondary metrics and guardrails are labeled so the team cannot quietly replace the primary outcome after seeing results.
The readiness handoff separates launch blockers, warnings, and post-launch monitoring. It includes links to the experiment, flag, queries, event definitions, test evidence, owners, and unresolved choices. Product and engineering approve traffic, rollout, and instrumentation changes. The agent can repeat the checklist after launch, but it cannot decide to ship a variant merely because an early chart points upward.
Expected handoff
The output should include readiness status, risk table, missing instrumentation, and next actions. It can become a Linear issue or product decision brief.